Loading Data

getwd()
## [1] "/Users/gracie/Documents/Psych_Stats/PsychStats/scripts"
psychData <- read.csv("~/Documents/Psych_Stats/Psych_Proj_Data - Sheet1.csv")

Loading necessary packages

library(tidyr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3     ✓ dplyr   1.0.3
## ✓ tibble  3.0.5     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ✓ purrr   0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(ggplot2)
library(modeest)
## Registered S3 method overwritten by 'rmutil':
##   method         from
##   print.response httr

Data cleaning

colnames(psychData)
##  [1] "pp"                "group"             "age"              
##  [4] "Neg_Urg"           "Pos_Urg"           "Lack_Premed"      
##  [7] "Lack_Perseverance" "Sensation_Seek"    "PCLTotal"         
## [10] "PCL_I"             "PCL_II"            "PCL_F1"           
## [13] "PCL_F2"            "PCL_F3"            "PCL_F4"
colnames(psychData)[1] <- "participant"

# Exploring data class
class(psychData$participant)
## [1] "integer"
  # This is an integer
class(psychData$group)
## [1] "integer"
  # This is an integer, but will need to be used as a factor for some analyses
class(psychData$age)
## [1] "integer"
  # This is an integer
class(psychData$Neg_Urg)
## [1] "integer"
  # This is an integer
class(psychData$Pos_Urg)
## [1] "integer"
  # This is an integer
class(psychData$Lack_Premed)
## [1] "integer"
  # This is an integer
class(psychData$Lack_Perseverance)
## [1] "integer"
  # This is an integer
class(psychData$Sensation_Seek)
## [1] "integer"
  # This is an integer
class(psychData$PCLTotal)
## [1] "numeric"
  # This is numeric
class(psychData$PCL_I)
## [1] "integer"
  # This is an integer, should be changed to numeric to match other facet scores
    psychData$PCL_I<- as.numeric(psychData$PCL_I)
    #check
    class(psychData$PCL_I)
## [1] "numeric"
class(psychData$PCL_II)
## [1] "numeric"
  # This is numeric
class(psychData$PCL_F1)
## [1] "integer"
  # This is an integer, should be changed to numeric to match other facet scores
    psychData$PCL_F1<- as.numeric(psychData$PCL_F1)
    #check
    class(psychData$PCL_F1)
## [1] "numeric"
class(psychData$PCL_F2)
## [1] "integer"
  # This is an integer, should be changed to numeric to match other facet scores
    psychData$PCL_F2<-  as.numeric(psychData$PCL_F2)
    #check
    class(psychData$PCL_F2)
## [1] "numeric"
class(psychData$PCL_F3)
## [1] "numeric"
  # This is numeric
class(psychData$PCL_F4)
## [1] "numeric"
  # This is numeric

Hypothesis #1:

#Hypothesis #1  
#If participants score high on both facets of Factor I on the PCL (Facet 1: interpersonal and Facet 2: affective traits), then they will also score high on sensation-seeking measures. 

#Ensuring that Facet 1 and Facet 2 are correlated
cor.test(psychData$PCL_F1, psychData$PCL_F2)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_F1 and psychData$PCL_F2
## t = 6.9227, df = 85, p-value = 7.873e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4462417 0.7200249
## sample estimates:
##       cor 
## 0.6004478
#Testing if there is a correlative relationship between sensation seeking scores and interpersonal traits
cor.test(psychData$Sensation_Seek, psychData$PCL_F1)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$Sensation_Seek and psychData$PCL_F1
## t = 0.68728, df = 85, p-value = 0.4938
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1384771  0.2805939
## sample estimates:
##        cor 
## 0.07433962
#Testing if there is a correlative relationship between sensation seeking scores and affective traits
cor.test(psychData$Sensation_Seek, psychData$PCL_F2)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$Sensation_Seek and psychData$PCL_F2
## t = -0.33206, df = 85, p-value = 0.7407
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2447857  0.1759892
## sample estimates:
##         cor 
## -0.03599341
#Testing if there is a correlative relationship between Factor 1 and sensation seeking scores
cor.test(psychData$Sensation_Seek, psychData$PCL_I)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$Sensation_Seek and psychData$PCL_I
## t = 0.23602, df = 85, p-value = 0.814
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1860601  0.2349727
## sample estimates:
##       cor 
## 0.0255911

Hypothesis #1 Visualizations

figure1<- ggplot(psychData, aes(PCL_F1, PCL_F2, color = Sensation_Seek)) +geom_point(size = 3) +theme_light()+ggtitle("Figure 1 \nInterpersonal and Affective Traits are Related") +xlab(label = "Interpersonal Traits") + ylab(label = "Affective Traits") + labs(color = "Sensation Seeking") + scale_colour_gradientn(colours = rainbow(2))+geom_smooth(method="lm", se = FALSE, color = "black")

ggplot(psychData, aes(PCL_F1, PCL_F2, color = Sensation_Seek)) +geom_point(size = 3) +theme_light()+ggtitle("Figure 1 \nInterpersonal and Affective Traits are Related") +xlab(label = "Interpersonal Traits") + ylab(label = "Affective Traits") + labs(color = "Sensation Seeking") + scale_colour_gradientn(colours = rainbow(2))+geom_smooth(method="lm", se = FALSE, color = "black")
## `geom_smooth()` using formula 'y ~ x'

ggplot(psychData, aes(PCL_F1, Sensation_Seek)) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

ggplot(psychData, aes(PCL_F2, Sensation_Seek)) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

figure2<- ggplot(psychData, aes(PCL_I, Sensation_Seek)) +geom_point(color = "#FF3333", size = 3, shape = 8) +theme_light() +ggtitle("Figure 2 \nPCL Factor 1 Scores Not Predictive of Sensation Seeking Scores") +xlab(label = "PCL Factor 1 Scores") + ylab(label = "Sensation Seeking Scores")

ggplot(psychData, aes(PCL_I, Sensation_Seek)) +geom_point(color = "#FF3333", size = 3, shape = 8) +theme_light() +ggtitle("Figure 2 \nPCL Factor 1 Scores Not Predictive of Sensation Seeking Scores") +xlab(label = "PCL Factor 1 Scores") + ylab(label = "Sensation Seeking Scores")+geom_smooth(method="lm", se = FALSE, color = "black")
## `geom_smooth()` using formula 'y ~ x'

Hypothesis #2:

#Hypothesis #2  
#If participants score high on both facets of Factor II on the PCL (lifestyle and antisocial traits), then they will also score low on lack of premeditation measures on the UPPS-P. 

#Ensuring that Facet 3 and Facet 4 are correlated
cor.test(psychData$PCL_F3, psychData$PCL_F4)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_F3 and psychData$PCL_F4
## t = 7.4388, df = 84, p-value = 7.957e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4827761 0.7428756
## sample estimates:
##       cor 
## 0.6301903
#Testing if there is a correlative relationship between lack of premeditation scores and lifestyletraits
cor.test(psychData$Lack_Premed, psychData$PCL_F3)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$Lack_Premed and psychData$PCL_F3
## t = 1.1646, df = 84, p-value = 0.2475
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.08817582  0.32913921
## sample estimates:
##       cor 
## 0.1260545
#Testing if there is a correlative relationship between lack of premeditation scores and ntisocial traits
cor.test(psychData$Lack_Premed, psychData$PCL_F4)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$Lack_Premed and psychData$PCL_F4
## t = 0.23572, df = 85, p-value = 0.8142
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1860916  0.2349419
## sample estimates:
##        cor 
## 0.02555859
#Testing if there is a correlative relationship between Factor 2 and lack of premeditation scores 
cor.test(psychData$Lack_Premed, psychData$PCL_II)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$Lack_Premed and psychData$PCL_II
## t = 0.90742, df = 85, p-value = 0.3668
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1150728  0.3023595
## sample estimates:
##        cor 
## 0.09794977

Hypothesis #2 Visualizations

figure3<- ggplot(psychData, aes(PCL_F3, PCL_F4, color = Lack_Premed)) +geom_point(size = 3)+theme_light() +ggtitle("Figure 3 \nLifestyle and Antisocial Traits are Related") +xlab(label = "Lifestyle Traits") + ylab(label = "Antisocial Traits")+ labs(color = "Lack of Premeditation") + scale_colour_gradientn(colours = rainbow(2))+geom_smooth(method="lm", se = FALSE, color = "black")

ggplot(psychData, aes(PCL_F3, PCL_F4, color = Lack_Premed)) +geom_point()+theme_light() +ggtitle("Figure 3 \nLifestyle and Antisocial Traits are Related") +xlab(label = "Lifestyle Traits") + ylab(label = "Antisocial Traits")+ labs(color = "Lack of Premeditation") + scale_colour_gradientn(colours = rainbow(2))
## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(psychData, aes(PCL_F3, Lack_Premed)) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(psychData, aes(PCL_F4, Lack_Premed)) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

figure4<- ggplot(psychData, aes(PCL_II, Lack_Premed)) +geom_point(color = "#FF3333", size = 3, shape = 8) +geom_smooth(method="lm", se = FALSE, color = "black")+theme_light() +ggtitle("Figure 4 \nPCL Factor 2 Scores Not Predictive of Lack of Premeditation Scores") +xlab(label = "PCL Factor 2 Scores") + ylab(label = "Lack of Premeditation Scores")

ggplot(psychData, aes(PCL_II, Lack_Premed)) +geom_point(color = "#FF3333", size = 3, shape = 8) +geom_smooth(method="lm", se = FALSE, color = "black")+theme_light() +ggtitle("Figure 4 \nPCL Factor 2 Scores Not Predictive of Lack of Premeditation Scores") +xlab(label = "PCL Factor 2 Scores") + ylab(label = "Lack of Premeditation Scores")
## `geom_smooth()` using formula 'y ~ x'

Hypothesis #3:

#Hypothesis #3  
#If participants score high overall on the PCL, then they will also score high in positive urgency measures on the UPPS-P. 

#Testing if there is a correlative relationship between total PCL Factor 1 and PCL Factor 2 
cor.test(psychData$PCL_I, psychData$PCL_II)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_I and psychData$PCL_II
## t = 5.0484, df = 85, p-value = 2.505e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2999877 0.6274537
## sample estimates:
##       cor 
## 0.4802858
#Testing if there is a correlative relationship between total PCL score and positive urgency scores
cor.test(psychData$PCLTotal, psychData$Pos_Urg)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCLTotal and psychData$Pos_Urg
## t = 1.7187, df = 85, p-value = 0.08931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.0284878  0.3792674
## sample estimates:
##       cor 
## 0.1832602
#Testing if there is a correlative relationship between total PCL Factor I and positive urgency scores
cor.test(psychData$PCL_I, psychData$Pos_Urg)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_I and psychData$Pos_Urg
## t = 0.20327, df = 85, p-value = 0.8394
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1894858  0.2316151
## sample estimates:
##        cor 
## 0.02204225
#Testing if there is a correlative relationship between total PCL Factor II and positive urgency scores
cor.test(psychData$PCL_II, psychData$Pos_Urg)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_II and psychData$Pos_Urg
## t = 2.7163, df = 85, p-value = 0.007996
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0765238 0.4655493
## sample estimates:
##       cor 
## 0.2826164
#Testing if there is a correlative relationship between total PCL score and negative urgency scores
cor.test(psychData$PCLTotal, psychData$Neg_Urg)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCLTotal and psychData$Neg_Urg
## t = 0.91118, df = 85, p-value = 0.3648
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1146720  0.3027284
## sample estimates:
##        cor 
## 0.09835194
#Testing if there is a correlative relationship between total PCL Factor I and negative urgency scores
cor.test(psychData$PCL_I, psychData$Neg_Urg)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_I and psychData$Neg_Urg
## t = -1.3135, df = 85, p-value = 0.1925
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.34154573  0.07173444
## sample estimates:
##        cor 
## -0.1410451
#Testing if there is a correlative relationship between total PCL Factor II and negative urgency scores
cor.test(psychData$PCL_II, psychData$Neg_Urg)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$PCL_II and psychData$Neg_Urg
## t = 2.4433, df = 85, p-value = 0.01662
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.04811469 0.44291473
## sample estimates:
##       cor 
## 0.2561666

Hypothesis #3 Visualizations

ggplot(psychData, aes(PCL_I, PCL_II)) + geom_point() +geom_smooth(method="lm")
## `geom_smooth()` using formula 'y ~ x'

figure6<- ggplot(psychData, aes(PCLTotal, Pos_Urg)) + geom_point(color = "#FF3333", size = 3, shape = 8)+ geom_smooth(method="lm", se = FALSE, color = "black") +ggtitle("Figure 6 \nPCL Total Scores Relation to Positive Urgency Measures") +xlab(label = "PCL Total Score") + ylab(label = "Positive Urgency Scores")

ggplot(psychData, aes(PCL_I, Pos_Urg)) + geom_point()

figure5<- ggplot(psychData, aes(PCL_II, Pos_Urg)) + geom_point(color = "#FF3333", size = 3, shape = 8) + geom_smooth(method="lm", se = FALSE, color = "black") +ggtitle("Figure 5 \nPCL Factor 2 Scores Predictive of Positive Urgency Measures") +xlab(label = "PCL Factor 2 Scores") + ylab(label = "Positive Urgency Scores")

ggplot(psychData, aes(PCLTotal, Neg_Urg)) + geom_point()

ggplot(psychData, aes(PCL_I, Neg_Urg)) + geom_point()

ggplot(psychData, aes(PCL_II, Neg_Urg)) + geom_point()

Exploratory Analyses: Do PCL scores differ by groups included in the subject sample (prison subject, patient subject)?

#Extra analyses

#Do PCL scores differ between groups?

cor.test(psychData$age, psychData$PCLTotal)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$age and psychData$PCLTotal
## t = -1.1049, df = 85, p-value = 0.2723
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32158330  0.09400875
## sample estimates:
##       cor 
## -0.118996
cor.test(psychData$group, psychData$PCLTotal)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$group and psychData$PCLTotal
## t = 9.7075, df = 85, p-value = 2e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6071877 0.8117551
## sample estimates:
##       cor 
## 0.7250942
cor.test(psychData$group, psychData$PCL_I)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$group and psychData$PCL_I
## t = 5.5085, df = 85, p-value = 3.788e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3388705 0.6530023
## sample estimates:
##       cor 
## 0.5129062
cor.test(psychData$group, psychData$PCL_II)
## 
##  Pearson's product-moment correlation
## 
## data:  psychData$group and psychData$PCL_II
## t = 7.4442, df = 85, p-value = 7.337e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4812557 0.7408299
## sample estimates:
##       cor 
## 0.6282179
t.test(PCLTotal~as.factor(group), data=psychData, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  PCLTotal by as.factor(group)
## t = -9.7075, df = 85, p-value = 2e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -16.56588 -10.93350
## sample estimates:
## mean in group 0 mean in group 1 
##        11.10294        24.85263
t.test(PCL_I~as.factor(group), data=psychData, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  PCL_I by as.factor(group)
## t = -5.5085, df = 85, p-value = 3.788e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.348614 -2.981107
## sample estimates:
## mean in group 0 mean in group 1 
##        4.808824        9.473684
t.test(PCL_II~as.factor(group), data=psychData, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  PCL_II by as.factor(group)
## t = -7.4442, df = 85, p-value = 7.337e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -8.242354 -4.767554
## sample estimates:
## mean in group 0 mean in group 1 
##        6.352941       12.857895
ggplot(psychData, aes(as.factor(group), PCLTotal))+geom_boxplot(color = "blue", fill = "lightyellow") +theme_light()+xlab(label = "Group \n 0 = Prison Sample \n 1 = Patient Sample") +ylab(label = "Total PCL Score")

Extra analyses visualizations

#Hypothesis 1:
ggplot(psychData, aes(PCL_F1, PCL_F2, color = Sensation_Seek)) +geom_point() +geom_smooth(method = "lm")+facet_wrap(~group)
## `geom_smooth()` using formula 'y ~ x'

ggplot(psychData, aes(PCL_F1, Sensation_Seek, color = as.factor(group))) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

ggplot(psychData, aes(PCL_F2, Sensation_Seek, color = as.factor(group))) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

ggplot(psychData, aes(PCL_I, Sensation_Seek, color = as.factor(group))) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

#Hypothesis 2:
ggplot(psychData, aes(PCL_F3, PCL_F4, color = Lack_Premed)) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(psychData, aes(PCL_F3, Lack_Premed, color = as.factor(group))) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(psychData, aes(PCL_F4, Lack_Premed, color = as.factor(group))) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

ggplot(psychData, aes(PCL_II, Lack_Premed, color = as.factor(group))) +geom_point() +geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

#Hypothesis 3:
ggplot(psychData, aes(PCLTotal, Pos_Urg, color = as.factor(group))) + geom_point()

ggplot(psychData, aes(PCL_I, Pos_Urg, color = as.factor(group))) + geom_point()

ggplot(psychData, aes(PCL_II, Pos_Urg, color = as.factor(group))) + geom_point()

ggplot(psychData, aes(PCLTotal, Neg_Urg, color = as.factor(group))) + geom_point()

ggplot(psychData, aes(PCL_I, Neg_Urg, color = as.factor(group))) + geom_point()

ggplot(psychData, aes(PCL_II, Neg_Urg, color = as.factor(group))) + geom_point()

Results:

The relationship between Facet 1, interpersonal traits, and Facet 2, affective traits, of the Psychopathy Checklist was tested with a Pearson’s Correlation test to ensure that they were accurately depicting Factor 1 on the PCL. This test yielded a positive correlation of 0.60 (p < 0.001) (Figure 1). When investigating the relationship between Sensation-Seeking measures and scores on the Psychopathy Check List Factor 1, the data indicated that there were not any relationships between these measures. A Pearson’s Product Moment Correlation test yielded a correlation of 0.03 (p = 0.814) (Figure 2). Moreover, the same test was conducted to assess the relationship between Sensation Seeking measures and both Facet 1 and Facet 2. These tests yielded correlations of 0.07 (p = 0.494) and -0.04 (p = 0.741), respectively.

figure1
## `geom_smooth()` using formula 'y ~ x'

figure2

The relationship between Facet 3, lifestyle traits, and Facet 4, antisocial traits, of the Psychopathy Checklist was tested with a Pearson’s Correlation test to ensure that they were accurately depicting Factor 2 on the PCL. This test yielded a positive correlation of 0.63 (p < 0.001) (Figure 3). When investigating the relationship between Lack of Premeditation measures and scores on the Psychopathy Check List Factor 2, the data indicated that there were not any relationships between these measures. A Pearson’s Product Moment Correlation test yielded a correlation of 0.098 (p = 0.367) (Figure 4). Moreover, the same test was conducted to assess the relationship between Sensation Seeking measures and both Facet 3 and Facet 4. These tests yielded correlations of 0.13 (p = 0.248) and 0.03 (p = 0.814), respectively.

figure3
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

figure4
## `geom_smooth()` using formula 'y ~ x'

The relationship between Factor 1 and and Factor 2 of the Psychopathy Checklist was tested with a Pearson’s Correlation test to identify if performance on each factor was related. This test yielded a positive correlation of 0.48 (p < 0.001). When investigating the relationship between Positive Urgency measures and scores on the Psychopathy Check List Factor 1, the data indicated that there were not any relationships between these measures (r = 0.02, p = 0.84). Additionally, when investigating the relationship between Positive Urgency measures and total score on the Psychopathy Check List, there was not a significant correlation either (r = 0.18, p = 0.09). However, there was a significant positive correlation between Factor 2 on the PCL and Positive Urgency measures (r = 0.28, p = 0.008), indicating that lifestyle and antisocial traits may be related to displays of positive urgency (Figure 5).

figure5
## `geom_smooth()` using formula 'y ~ x'